Cauda EVs miRNAs analysis using data from both runs
Libraries required for data analysis
source("src.R")Data: read counts table from both runs
counts_total_run3 <- read.table(
file = "run3_excerpt_counts/exceRpt_miRNA_ReadCounts.txt",
header = TRUE, sep = "", row.names = 1
)
counts_total_run4 <- read.table(
file = "run4_excerpt_counts_/exceRpt_miRNA_ReadCounts.txt",
header = TRUE, sep = "", row.names = 1
)
colnames(counts_total_run4) <- paste0(colnames(counts_total_run4), "_2")
df <- merge(counts_total_run3, counts_total_run4, by = "row.names")
rownames(df) <- df$Row.names
df <- df[, -1]
knitr::kable(head(df))| MS28F1_sample2 | MS28F1_sample3 | MS28F1_sample4 | MS28F1_sample5 | MS28F1_sample6 | MS28F1_sample7 | MS28F1_sample8 | MS28F1_sample9 | MS28F1_sample11 | MS28F1_sample14 | MS28F1_sample2_2 | MS28F1_sample3_2 | MS28F1_sample4_2 | MS28F1_sample5_2 | MS28F1_sample6_2 | MS28F1_sample7_2 | MS28F1_sample8_2 | MS28F1_sample9_2 | MS28F1_sample11_2 | MS28F1_sample14_2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mmu-let-7a-1 | 1.1666667 | 1.5 | 1.666667 | 1.1666667 | 1.3333333 | 0.000 | 0.000 | 2.000000 | 7.5833333 | 4.1666667 | 2.9166667 | 1.750 | 1.000 | 1.500 | 0.9166667 | 0.8333333 | 1.916667 | 2 | 3.333333 | 4.0000000 |
| mmu-let-7a-1-3p|mmu-let-7c-2-3p | 19.0000000 | 34.0 | 21.500000 | 38.0000000 | 42.0000000 | 14.000 | 26.000 | 42.000000 | 95.0000000 | 78.0000000 | 32.0000000 | 53.000 | 31.000 | 42.000 | 20.0000000 | 32.0000000 | 45.000000 | 65 | 103.500000 | 90.5000000 |
| mmu-let-7a-2 | 0.6666667 | 1.0 | 2.666667 | 0.6666667 | 0.3333333 | 0.000 | 0.000 | 3.166667 | 0.5833333 | 0.6666667 | 2.9166667 | 1.750 | 0.000 | 0.500 | 1.9166667 | 1.3333333 | 2.416667 | 5 | 2.500000 | 0.3333333 |
| mmu-let-7a-5p | 819.5000000 | 2422.5 | 1568.833333 | 2313.3333333 | 1867.6666667 | 1422.833 | 1469.583 | 4181.500000 | 5725.0000000 | 4915.0000000 | 3184.1666667 | 4260.667 | 2186.333 | 4359.667 | 2123.3333333 | 3710.6666667 | 4274.666667 | 5608 | 7622.750000 | 7111.8333333 |
| mmu-let-7b | 0.0000000 | 0.0 | 0.000000 | 0.0000000 | 0.0000000 | 0.000 | 0.250 | 0.000000 | 0.0000000 | 0.0000000 | 0.3333333 | 0.000 | 0.000 | 0.000 | 0.0000000 | 0.0000000 | 0.000000 | 0 | 0.250000 | 0.0000000 |
| mmu-let-7b-3p | 10.0000000 | 7.0 | 9.000000 | 6.0000000 | 10.0000000 | 2.000 | 4.000 | 10.000000 | 18.0000000 | 23.0000000 | 10.0000000 | 7.000 | 10.000 | 17.000 | 0.0000000 | 7.0000000 | 9.000000 | 10 | 22.000000 | 22.0000000 |
Specifying the group & batch while filterByExprs()
counts_total <- df
d <- data.frame(
row.names = colnames(counts_total),
group = c(
"MSUS", "CTRL", "MSUS", "CTRL", "MSUS",
"CTRL", "MSUS", "CTRL", "CTRL", "MSUS",
"MSUS", "CTRL", "MSUS", "CTRL", "MSUS",
"CTRL", "MSUS", "CTRL", "CTRL", "MSUS"
),
batch = c(
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2
)
)
counts_total[is.na(counts_total)] <- 0
dds <- calcNormFactors(DGEList(counts_total), method = "TMM")
dds$samples$group <- c(
"MSUS", "CTRL", "MSUS", "CTRL", "MSUS", "CTRL", "MSUS", "CTRL", "CTRL",
"MSUS", "MSUS", "CTRL", "MSUS", "CTRL", "MSUS", "CTRL", "MSUS", "CTRL",
"CTRL", "MSUS"
)
dds$samples$batch <- c(
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2
)
mm <- model.matrix(~ batch + group, data = d)
### specifying thebatch while filtering by expression
dds <- dds[which(filterByExpr(dds, design = mm, min.count = 20, min.prop = 0.8)), ]
# normalization by counts per million
en <- log1p(edgeR::cpm(dds))PCA plots
Samples colored by group and shaped by batched
plgINS::plPCA(en,
colorBy = dds$samples$group, add.labels = FALSE,
points.size = 12, shapeBy = paste0("Batch", dds$samples$batch)
)Samples colored by library sizes and shaped by group
plgINS::plPCA(en,
colorBy = dds$samples$lib.size, add.labels = FALSE,
points.size = 12, shapeBy = dds$samples$group
)Samples colored by library sizes and shaped by batches
plgINS::plPCA(en,
colorBy = dds$samples$lib.size, add.labels = FALSE,
points.size = 12, shapeBy = dds$samples$batch
)Without RUVSeq:
mm <- model.matrix(~ dds$samples$batch + dds$samples$group, data = d)
dds <- estimateDisp(dds, mm)
fit <- glmLRT(glmFit(dds, mm), coef = "dds$samples$groupMSUS")
res_without_ruvseq <- as.data.frame(topTags(fit, Inf))
knitr::kable(head(res_without_ruvseq, n = 20))| logFC | logCPM | LR | PValue | FDR | |
|---|---|---|---|---|---|
| mmu-miR-31-5p | -0.5630570 | 9.993834 | 14.030932 | 0.0001798 | 0.0341794 |
| mmu-miR-878-5p | 0.5510667 | 7.622078 | 12.917534 | 0.0003255 | 0.0341794 |
| mmu-miR-155-5p | -0.5886507 | 7.892908 | 12.102538 | 0.0005035 | 0.0352473 |
| mmu-miR-34c-5p | 0.4318036 | 12.490877 | 9.725028 | 0.0018178 | 0.0954321 |
| mmu-let-7a-5p | -0.3683619 | 13.378312 | 9.099385 | 0.0025570 | 0.1073921 |
| mmu-let-7c-5p | -0.3242745 | 14.170447 | 8.286976 | 0.0039930 | 0.1338609 |
| mmu-miR-881-3p | 0.5117907 | 11.466915 | 8.085521 | 0.0044620 | 0.1338609 |
| mmu-miR-484 | -0.5904084 | 7.027135 | 7.268392 | 0.0070179 | 0.1663234 |
| mmu-let-7d-5p | -0.3214286 | 12.059820 | 6.929838 | 0.0084769 | 0.1663234 |
| mmu-miR-223-3p | -0.4271443 | 7.623028 | 6.902397 | 0.0086080 | 0.1663234 |
| mmu-let-7b-5p | -0.3097913 | 12.513706 | 6.880898 | 0.0087122 | 0.1663234 |
| mmu-let-7f-5p | -0.2362616 | 15.094629 | 6.637108 | 0.0099876 | 0.1747829 |
| mmu-miR-339-5p | -0.3058132 | 8.661776 | 6.150944 | 0.0131342 | 0.2121678 |
| mmu-let-7g-5p | -0.1996120 | 14.499044 | 5.439798 | 0.0196829 | 0.2480974 |
| mmu-miR-9-5p | 0.3439249 | 7.798056 | 5.402020 | 0.0201135 | 0.2480974 |
| mmu-miR-381-3p | 0.6938547 | 6.549209 | 5.298593 | 0.0213427 | 0.2480974 |
| mmu-miR-150-5p | -0.4484746 | 10.173697 | 5.222411 | 0.0222976 | 0.2480974 |
| mmu-miR-690 | -0.3084864 | 8.671606 | 5.112553 | 0.0237533 | 0.2480974 |
| mmu-miR-218-5p | 0.2785387 | 8.825840 | 5.069562 | 0.0243495 | 0.2480974 |
| mmu-miR-148a-5p | -0.3500173 | 6.582395 | 5.044113 | 0.0247098 | 0.2480974 |
Differential expression analysis with RUVSeq (SV=1)
re <- RUVSeq::RUVs(en,
cIdx = row.names(en), k = 1, scIdx =
RUVSeq::makeGroups(d$group), isLog = TRUE
)
d$SV1 <- re$W[, 1]
mm <- model.matrix(~ d$SV1 + dds$samples$batch + dds$samples$group, data = d)
dds <- estimateDisp(dds, mm)
fit <- glmLRT(glmFit(dds, mm), coef = "dds$samples$groupMSUS")
res_with_ruvseq1 <- as.data.frame(topTags(fit, Inf))
knitr::kable(head(res_with_ruvseq1, n = 20))| logFC | logCPM | LR | PValue | FDR | |
|---|---|---|---|---|---|
| mmu-miR-871-3p | 0.4224445 | 7.865090 | 11.917353 | 0.0005561 | 0.0721259 |
| mmu-miR-31-5p | -0.3876365 | 9.998799 | 11.067851 | 0.0008784 | 0.0721259 |
| mmu-miR-155-5p | -0.4509784 | 7.903016 | 10.270414 | 0.0013518 | 0.0721259 |
| mmu-miR-881-3p | 0.2898815 | 11.465624 | 9.947026 | 0.0016111 | 0.0721259 |
| mmu-miR-878-5p | 0.4517734 | 7.616360 | 9.829572 | 0.0017173 | 0.0721259 |
| mmu-let-7f-5p | -0.2539420 | 15.094684 | 8.265118 | 0.0040414 | 0.1414491 |
| mmu-let-7a-5p | -0.3252566 | 13.378699 | 7.854573 | 0.0050692 | 0.1507945 |
| mmu-miR-34c-5p | 0.3699764 | 12.490816 | 7.628736 | 0.0057446 | 0.1507945 |
| mmu-let-7c-5p | -0.2660684 | 14.170724 | 6.590841 | 0.0102505 | 0.2391777 |
| mmu-let-7d-5p | -0.2957245 | 12.060525 | 6.254407 | 0.0123885 | 0.2514734 |
| mmu-miR-148a-5p | -0.3860498 | 6.582776 | 6.099744 | 0.0135201 | 0.2514734 |
| mmu-miR-486a-5p|mmu-miR-486b-5p | 0.4242328 | 9.175211 | 5.992121 | 0.0143699 | 0.2514734 |
| mmu-miR-223-3p | -0.3918551 | 7.627599 | 5.758552 | 0.0164086 | 0.2650618 |
| mmu-miR-142a-5p | -0.3388176 | 7.860399 | 5.331804 | 0.0209397 | 0.2995186 |
| mmu-miR-484 | -0.4464519 | 7.035887 | 5.294393 | 0.0213942 | 0.2995186 |
| mmu-miR-182-5p | -0.1858805 | 11.537582 | 5.084665 | 0.0241383 | 0.3168156 |
| mmu-miR-183-5p | -0.2106265 | 11.356649 | 4.759219 | 0.0291418 | 0.3395679 |
| mmu-let-7g-5p | -0.1769275 | 14.499171 | 4.704360 | 0.0300862 | 0.3395679 |
| mmu-miR-181d-5p | 0.2729988 | 7.492399 | 4.642132 | 0.0311962 | 0.3395679 |
| mmu-miR-467a-5p|mmu-miR-467b-5p | -0.3391907 | 6.822511 | 4.509472 | 0.0337076 | 0.3395679 |
PCA plot with 1 SV
Samples colored by group and shaped by batched
plgINS::plPCA(re$normalizedCounts,
colorBy = dds$samples$group, add.labels = FALSE,
points.size = 12, shapeBy = paste0("Batch", dds$samples$batch)
)Samples colored by library sizes and shaped by group
plgINS::plPCA(re$normalizedCounts,
colorBy = dds$samples$lib.size, add.labels = FALSE,
points.size = 12, shapeBy = dds$samples$group
)Samples colored by library sizes and shaped by batches
plgINS::plPCA(re$normalizedCounts,
colorBy = dds$samples$lib.size, add.labels = FALSE,
points.size = 12, shapeBy = dds$samples$batch
)References
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SessionInfo
devtools::session_info() %>%
details::details()
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.0.4 (2021-02-15)
os Ubuntu 16.04.7 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate de_DE.UTF-8
ctype de_DE.UTF-8
tz Europe/Zurich
date 2021-06-28
─ Packages ───────────────────────────────────────────────────────────────────
! package * version date lib source
annotate 1.68.0 2020-10-27 [1] Bioconductor
AnnotationDbi * 1.52.0 2020-10-27 [1] Bioconductor
AnnotationFilter 1.14.0 2020-10-27 [1] Bioconductor
AnnotationHub 2.22.1 2021-04-16 [1] Bioconductor
aroma.light 3.20.0 2020-10-27 [1] Bioconductor
ash 1.0-15 2015-09-01 [1] CRAN (R 4.0.4)
askpass 1.1 2019-01-13 [1] CRAN (R 4.0.4)
assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.4)
backports 1.2.1 2020-12-09 [1] CRAN (R 4.0.4)
bayestestR 0.9.0 2021-04-08 [1] CRAN (R 4.0.4)
beeswarm 0.3.1 2021-03-07 [1] CRAN (R 4.0.4)
Biobase * 2.50.0 2020-10-27 [1] Bioconductor
BiocFileCache 1.14.0 2020-10-27 [1] Bioconductor
BiocGenerics * 0.36.1 2021-04-16 [1] Bioconductor
BiocManager 1.30.12 2021-03-28 [1] CRAN (R 4.0.4)
BiocParallel * 1.24.1 2020-11-06 [1] Bioconductor
BiocVersion 3.12.0 2020-04-27 [1] Bioconductor
biomaRt * 2.46.3 2021-02-09 [1] Bioconductor
Biostrings * 2.58.0 2020-10-27 [1] Bioconductor
bit 4.0.4 2020-08-04 [1] CRAN (R 4.0.4)
bit64 4.0.5 2020-08-30 [1] CRAN (R 4.0.4)
bitops 1.0-7 2021-04-24 [1] CRAN (R 4.0.4)
blob 1.2.1 2020-01-20 [1] CRAN (R 4.0.4)
bookdown 0.22 2021-04-22 [1] CRAN (R 4.0.4)
boot 1.3-27 2021-02-12 [1] CRAN (R 4.0.4)
broom 0.7.7 2021-06-13 [1] CRAN (R 4.0.4)
bslib 0.2.4 2021-01-25 [1] CRAN (R 4.0.4)
cachem 1.0.4 2021-02-13 [1] CRAN (R 4.0.4)
callr 3.7.0 2021-04-20 [1] CRAN (R 4.0.4)
caTools 1.18.2 2021-03-28 [1] CRAN (R 4.0.4)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.0.4)
cli 2.5.0 2021-04-26 [1] CRAN (R 4.0.4)
clipr 0.7.1 2020-10-08 [1] CRAN (R 4.0.4)
coda 0.19-4 2020-09-30 [1] CRAN (R 4.0.4)
codetools 0.2-18 2020-11-04 [1] CRAN (R 4.0.4)
colorRamps 2.3 2012-10-29 [1] CRAN (R 4.0.4)
colorspace 2.0-0 2020-11-11 [1] CRAN (R 4.0.4)
conquer 1.0.2 2020-08-27 [1] CRAN (R 4.0.4)
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P ── Loaded and on-disk path mismatch.